Prognostic Accuracy Research Articles

Tumour growth rate predicts overall survival in patients with recurrent WHO grade 4 glioma.

Accurate prognostication may aid in the selection of patients who will benefit from surgery at recurrent WHO grade 4 glioma. This study aimed to evaluate the role of serial tumour volumetric measurements for prognostication at first tumour recurrence. We retrospectively analyzed patients with histologically-diagnosed WHO grade 4 glioma at initial and at first tumour recurrence at a tertiary hospital between May 2000 and September 2018. We performed auto-segmentation using ITK-SNAP software, followed by manual adjustment to measure serial contrast-enhanced T1W (CE-T1W) and T2W lesional volume changes on all MRI images performed between initial resection and repeat surgery. Thirty patients met inclusion criteria; the median overall survival using Kaplan-Meier analysis from second surgery was 10.5 months. Seventeen (56.7%) patients received treatment post second surgery. Univariate cox regression analysis showed that greater rate of increase in lesional volume on CE-T1W (HR = 2.57; 95% CI [1.18, 5.57]; p = 0.02) in the last 2 MRI scans leading up to the second surgery was associated with a higher mortality likelihood. Patients with higher Karnofsky Performance Score (KPS) (HR = 0.97; 95% CI [0.95, 0.99]; p = 0.01) and who received further treatment following second surgery (HR = 0.43; 95% CI [0.19, 0.98]; p = 0.04) were shown to have a better survival. Higher rate of CE-T1W lesional growth on the last 2 MRI images prior to surgery at recurrence was associated with increase mortality risk. A larger prospective study is required to determine and validate the threshold to distinguish rapidly progressive tumour with poor prognosis.

Predicting Individual Hearing-Aid Preference From Self-Reported Listening Experiences in Daily Life.

The study compared the utility of two approaches for collecting real-world listening experiences to predict hearing-aid preference: a retrospective questionnaire (Speech, Spatial, and Qualities of Hearing Scale [SSQ]) and in-situ Ecological Momentary Assessment (EMA). The rationale being that each approach likely provides different and yet complementary information. In addition, it was examined how self-reported listening activity and hearing-aid data-logging can augment EMAs for individualized and contextualized hearing outcome assessments. Experienced hearing-aid users (N = 40) with mild-to-moderate symmetrical sensorineural hearing loss completed the SSQ questionnaire and gave repeated EMAs for two wear periods of 2-weeks each with two different hearing-aid models that differed mainly in their noise reduction technology. The EMAs were linked to a self-reported listening activity and sound environment parameters (from hearing-aid data-logging) recorded at the time of EMA completion. Wear order was randomized by hearing-aid model. Linear mixed-effects models and Random Forest models with five-fold cross-validation were used to assess the statistical associations between listening experiences and end-of-trial preferences, and to evaluate how accurately EMAs predicted preference within individuals. Only 6 of the 49 SSQ items significantly discriminated between responses made for the end-of-trial preferred versus nonpreferred hearing-aid model. For the EMAs, questions related to perception of the sound from the hearing aids were all significantly associated with preference, and these associations were strongest in EMAs completed in sound environments with predominantly low SNR and listening activities related to television, people talking, nonspecific listening, and music listening. Mean differences in listening experiences from SSQ and EMA correctly predicted preference in 71.8% and 72.5% of included participants, respectively. However, a prognostic classification of single EMAs into end-of-trial preference with a Random Forest model achieved a 93.8% accuracy when contextual information was included. SSQ and EMA predicted preference equally well when considering mean differences, however, EMAs had a high prognostic classifications accuracy due to the repeated-measures nature, which make them ideal for individualized hearing outcome investigations, especially when responses are combined with contextual information about the sound environment.

Real-world outcomes of newly diagnosed AML treated with venetoclax and azacitidine or low-dose cytarabine in the UK NHS

AbstractVenetoclax with azacitidine is the standard of care for patients with acute myeloid leukemia (AML) who are unfit for intensive chemotherapy; however, uncertainties remain regarding the treatment schedule, accurate prognostication, and outcomes for patients treated outside clinical trials. The option of venetoclax with low-dose cytarabine (LDAC) is also available; however, it is not clear for which patients it may be a useful alternative. Here, we report a large real-world cohort of 654 patients treated in 53 UK hospitals with either venetoclax and azacitidine (n = 587) or LDAC (n = 67). The median age was 73 years, and 59% had de novo AML. Most patients received 100 mg of venetoclax with an azole antifungal. In cycle 1, patients spent a median of 14 days in the hospital, and 85% required red cell transfusion, 59% platelet transfusion, and 63% required IV antibiotics. Supportive care requirements significantly reduced after the first cycle. Patients receiving venetoclax-azacitidine had a complete remission (CR)/CR with incomplete hematological recovery rate of 67%, day 30 and day 60 mortality of 5% and 8%, respectively, and median overall survival of 13.6 months. Mutations in NPM1, RUNX1, STAG2, and IDH2 were associated with improved survival, whereas age, secondary and therapy-related AML, +8, MECOM rearrangements, complex karyotype, ASXL1, and KIT mutations were associated with poorer survival. Prognostic systems derived specifically for patients treated with venetoclax-azacitidine performed better than the European LeukemiaNet and Medical Research Council classifications; however, improved risk classifications are still required. In the 149 patients with NPM1 mutated AML, outcomes were similar for those treated with venetoclax-azacitidine and venetoclax-LDAC.

Regional Brain Net Water Uptake in Computed Tomography after Cardiac Arrest – A Novel Biomarker for Neuroprognostication

BackgroundSelective water uptake by neurons and glial cells and subsequent brain tissue oedema are key pathophysiological processes of hypoxic-ischemic encephalopathy (HIE) after cardiac arrest (CA). Although brain computed tomography (CT) is widely used to assess the severity of HIE, changes of brain radiodensity over time have not been investigated. These could be used to quantify regional brain net water uptake (NWU), a potential prognostic biomarker. MethodsWe conducted an observational prognostic accuracy study including a derivation (single center cardiac arrest registry) and a validation (international multicenter TTM2 trial) cohort. Early (<6 h) and follow-up (>24 h) head CTs of CA patients were used to determine regional NWU for grey and white matter regions after co-registration with a brain atlas. Neurological outcome was dichotomized as good versus poor using the Cerebral Performance Category Scale (CPC) in the derivation cohort and Modified Rankin Scale (mRS) in the validation cohort. ResultsWe included 115 patients (81 derivation, 34 validation) with out-of-hospital (OHCA) and in-hospital cardiac arrest (IHCA). Regional brain water content remained unchanged in patients with good outcome. In patients with poor neurological outcome, we found considerable regional water uptake with the strongest effect in the basal ganglia. NWU >8% in the putamen and caudate nucleus predicted poor outcome with 100% specificity (95%-CI: 86–100%) and 43% (moderate) sensitivity (95%-CI: 31–56%). ConclusionThis pilot study indicates that NWU derived from serial head CTs is a promising novel biomarker for outcome prediction after CA. NWU >8% in basal ganglia grey matter regions predicted poor outcome while absence of NWU indicated good outcome. NWU and follow-up CTs should be investigated in larger, prospective trials with standardized CT acquisition protocols.

#2643 CD4+ T cells are associated with mortality in males with type 2 cardiorenal syndrome independently of the Seattle Heart Failure Model score

Abstract Background and Aims Immune system dysregulation appears to contribute to the pathogenesis and prognosis of the reciprocal dysfunction that occurs in cardiorenal syndrome (CRS). However, there is little evidence available regarding the potential alterations of the cellular components of the immune system and their clinical significance in patients with CRS. The Seattle Heart Failure Model (SHFM) is a well-established, multivariable risk model for predicting survival in heart failure (HF) patients, including lymphocytes but not indices of kidney function among its variables. The aim of our prospective study was to examine the incremental value of a selected panel of immune cell subsets on the mortality risk prediction of the SHFM in a cohort of type 2 CRS patients. Method 36 stable male patients with CRS type II were included in the final analysis of our prospective study. Exclusion criteria included history of malignancy, autoimmunity and active or chronic infections. The peripheral blood immune cell subsets CD14++CD16-, CD14++CD16+ and CD14+CD16++ absolute values and percentages out of total monocytes and NK cells (CD3+CD16+56+), CD3-CD19+ B lymphocytes, CD3+ CD4+ T cells, CD3+CD8+ T cells and T regulatory cells (Tregs) (CD4+CD25+FoxP3+) absolute values and percentages out of total lymphocytes were measured by flow cytometry. The Seattle Heart Failure Model (SHFM) score was derived from the online updated calculator https://depts.washington.edu/shfm. After baseline evaluation, patients were followed until the end of the established observation period, progression to end-stage kidney disease (ESKD) or until the study endpoint was reached, which was defined as a combined outcome of all-cause mortality and cardiovascular mortality. Logistic regression analysis was used to identify predictors of observed mortality and SHFM predicted mortality among various cell subtypes in CRS patients. Comparison of the prognostic accuracy of various models was performed using area under the curve (AUC) analysis. Results The mean age of our cohort was 73 ± 9 years with an eGFR 36.8 ± 12.9 ml/min/1.73 m2. Most patients had HF with reduced ejection fraction (52%) and described symptoms of HF indicative for NYHA class II and III (55% and 45%) respectively. During a mean follow-up of 29.8 ± 3.4 months, 21 patients (i.e. 58%) died while 5 patients (i.e. 14%) progressed to ESKD. The score for 1-year mortality derived from SHFM was significantly correlated with the observed mortality (r = 0.500, p = 0.002) but not with the occurrence of ESKD during follow-up. Total lymphocytes (r −0.516, p = 0.001), T-lymphocytes (r −0.543, p = 0.002), CD4+ T cells (r −0.518, p = 0.001), CD8+ T cells (r −0.409, p = 0.013) and T-regs (r −0.376, p = 0.024) counts were all significantly related to the mortality score derived from SHFM apart from relating to the observed mortality in our population (p &amp;lt; 0.05 for all relations with observed mortality). The proinflammatory, intermediate CD14++CD16+ monocytes counts were associated with the observed mortality (r 0.383, p = 0.021) but not with SHFM score derived mortality. After adjustment for the SHFM score derived mortality, only the CD4+ T cell count was significantly and independently associated with the observed mortality (OR 0.99, p = 0.045) whereas T-regs count showed a non-significant trend for an independent association with the observed mortality after adjustment (OR 0.95, p = 0.063). The addition of CD4+ T cells count, or Tregs count on SHFM improved AUC for mortality but not significantly [AUC SHFM 0.833, SHFM + CD4+ T cells 0.933 (p = 0.155 vs SHFM), SHFM + Tregs 0.933 (p = 0.145 vs SHFM)]. Conclusion In a small group of male patients with type 2 CRS, CD4+ T lymphocyte count was independently associated with mortality even after adjustment for the established mortality score derived from SHFM. However, the combination of both SHFM and CD4+ T cells count did not improve significantly the accuracy of the prediction models, probably attributed to our small sample size. Larger clinical studies are needed to validate the importance of specific lymphocyte subpopulations in the prognosis and management of patients with cardiorenal syndrome.

E-cadherin expression and gene expression profiles in corticotroph pituitary neuroendocrine tumor subtypes.

Corticotroph adenomas/pituitary neuroendocrine tumors (PitNETs) are associated with significant morbidity and mortality. Predictors of tumor behavior have not shown high prognostic accuracy. For somatotroph adenomas/PitNETs, E-cadherin expression correlates strongly with prognosis. E-cadherin expression has not been investigated in other PitNETs. A retrospective chart review of adults with corticotroph adenomas/PitNETs was conducted to assess correlation between E-cadherin expression and tumor characteristics. In addition, gene expression microarray was performed in subset of tumors (n = 16). Seventy-seven patients were identified; 71% were female, with median age of cohort 45.2 years. Seventy-five percent had macroadenomas, of which 22% were hormonally active. Ninety-five percent of microadenomas were hormonally active. Adrenocorticotropic hormone granulation pattern by IHC identified 63% as densely granulated (DG) and 34% as sparsely granulated (SG). All microadenomas were DG (p < .001); 50% of macroadenomas were DG associated with increased tumor invasion compared to SG. E-cadherin IHC was positive in 80%, diminished in 17%, and absent in 20% and did not correlate with corticotroph PitNETs subtype, size, or prognosis. In contrast to the distinct transcriptomes of corticotroph PitNETs and normal pituitaries, a comparison of clinically active and silent corticotroph PitNETs demonstrated similar molecular signatures indicating their common origin, but with unique differences related to their secretory status.

A Focus on the Pathophysiology of Adrenomedullin Expression: Endothelitis and Organ Damage in Severe Viral and Bacterial Infections.

Adrenomedullin (ADM) is a peptide hormone produced primarily in the adrenal glands, playing a crucial role in various physiological processes. As well as improving vascular integrity and decreasing vascular permeability, ADM acts as a vasodilator, positive inotrope, diuretic, natriuretic and bronchodilator, antagonizing angiotensin II by inhibiting aldosterone secretion. ADM also has antihypertrophic, anti-apoptotic, antifibrotic, antioxidant, angiogenic and immunoregulatory effects and antimicrobial properties. ADM expression is upregulated by hypoxia, inflammation-inducing cytokines, viral or bacterial substances, strength of shear stress, and leakage of blood vessels. These pathological conditions are established during systemic inflammation that can result from infections, surgery, trauma/accidents or burns. The ability to rapidly identify infections and the prognostic, predictive power makes it a valuable tool in severe viral and bacterial infections burdened by high incidence and mortality. This review sheds light on the pathophysiological processes that in severe viral or bacterial infections cause endothelitis up to the development of organ damage, the resulting increase in ADM levels dosed through its more stable peptide mid-regional proadrenomedullin (MR-proADM), the most significant studies that attest to its diagnostic and prognostic accuracy in highlighting the severity of viral or bacterial infections and appropriate therapeutic insights.

Advances in dilated cardiomyopathy evaluation via cardiac MRI diagnosis and prognosis: a literature review

With a variety of aetiologies and clinical presentations, Dilated Cardiomyopathy (DCM) presents considerable hurdles for both diagnosis and treatment. Cardiac magnetic resonance imaging (CMR) has become a useful diagnostic technique for DCM, providing information on the anatomy, physiology, and tissue properties of the heart. Using both well-established and recently developed CMR techniques, this review investigates the function of CMR in the diagnosis and treatment of idiopathic DCM. Using reliable databases, a thorough search of the literature was carried out with an emphasis on papers released between 2014 and 2024. English-language literature examining the clinical uses of CMR in idiopathic DCM were included in the inclusion criteria. A vital component of the assessment of dilated cardiomyopathy (DCM) is cardiac magnetic resonance imaging (CMR), which provides thorough insights into the anatomy and function of the heart. Precise evaluation of tissue properties, ventricular function, and cardiac shape is made possible by CMR, which helps DCM patients get an accurate diagnosis and risk assessment. The use of advanced CMR methods, such as T1 mapping and late gadolinium enhancement (LGE) imaging, yields significant insights into the underlying aetiologies and myocardial fibrosis. Furthermore, customized prognostic evaluations are provided by CMR-based risk stratification algorithms that include many factors, improving clinical decision-making in DCM treatment. Continuous developments are expected to improve prognostic utility and accuracy, which will need efforts to lower prices and increase accessibility in order to achieve better patient outcomes and wider clinical usage

Prognostic stratification of glioblastoma patients by unsupervised clustering of morphology patterns on whole slide images furthering our disease understanding.

Glioblastoma (GBM) is a highly aggressive malignant tumor of the central nervous system that displays varying molecular and morphological profiles, leading to challenging prognostic assessments. Stratifying GBM patients according to overall survival (OS) from H&E-stained whole slide images (WSI) using advanced computational methods is challenging, but with direct clinical implications. This work is focusing on GBM (IDH-wildtype, CNS WHO Gr.4) cases, identified from the TCGA-GBM and TCGA-LGG collections after considering the 2021 WHO classification criteria. The proposed approach starts with patch extraction in each WSI, followed by comprehensive patch-level curation to discard artifactual content, i.e., glass reflections, pen markings, dust on the slide, and tissue tearing. Each patch is then computationally described as a feature vector defined by a pre-trained VGG16 convolutional neural network. Principal component analysis provides a feature representation of reduced dimensionality, further facilitating identification of distinct groups of morphology patterns, via unsupervised k-means clustering. The optimal number of clusters, according to cluster reproducibility and separability, is automatically determined based on the rand index and silhouette coefficient, respectively. Our proposed approach achieved prognostic stratification accuracy of 83.33% on a multi-institutional independent unseen hold-out test set with sensitivity and specificity of 83.33%. We hypothesize that the quantification of these clusters of morphology patterns, reflect the tumor's spatial heterogeneity and yield prognostic relevant information to distinguish between short and long survivors using a decision tree classifier. The interpretability analysis of the obtained results can contribute to furthering and quantifying our understanding of GBM and potentially improving our diagnostic and prognostic predictions.

Anti-apolipoprotein A-1 IgG, incident cardiovascular events, and lipid paradox in rheumatoid arthritis.

To validate the prognostic accuracy of anti-apolipoprotein A-1 (AAA1) IgG for incident major adverse cardiovascular (CV) events (MACE) in rheumatoid arthritis (RA) and study their associations with the lipid paradox at a multicentric scale. Baseline AAA1 IgG, lipid profile, atherogenic indexes, and cardiac biomarkers were measured on the serum of 1,472 patients with RA included in the prospective Swiss Clinical Quality Management registry with a median follow-up duration of 4.4 years. MACE was the primary endpoint defined as CV death, incident fatal or non-fatal stroke, or myocardial infarction (MI), while elective coronary revascularization (ECR) was the secondary endpoint. Discriminant accuracy and incidence rate ratios (IRR) were respectively assessed using C-statistics and Poisson regression models. During follow-up, 2.4% (35/1,472) of patients had a MACE, consisting of 6 CV deaths, 11 MIs, and 18 strokes; ECR occurred in 2.1% (31/1,472) of patients. C-statistics indicated that AAA1 had a significant discriminant accuracy for incident MACE [C-statistics: 0.60, 95% confidence interval (95% CI): 0.57-0.98, p = 0.03], mostly driven by CV deaths (C-statistics: 0.77; 95% CI: 0.57-0.98, p = 0.01). IRR indicated that each unit of AAA1 IgG increase was associated with a fivefold incident CV death rate, independent of models' adjustments. At the predefined and validated cut-off, AAA1 displayed negative predictive values above 97% for MACE. AAA1 inversely correlated with total and HDL cholesterol. AAA1 independently predicts CV deaths, and marginally MACE in RA. Further investigations are requested to ascertain whether AAA1 could enhance CV risk stratification by identifying patients with RA at low CV risk.

A validated web-application (GFDC) for automatic classification of glaucomatous visual field defects using Hodapp-Parrish-Anderson criteria.

Subjectivity and ambiguity of visual field classification limits the accuracy and reliability of glaucoma diagnosis, prognostication, and management decisions. Standardised rules for classifying glaucomatous visual field defects exist, but these are labour-intensive and therefore impractical for day-to-day clinical work. Here a web-application, Glaucoma Field Defect Classifier (GFDC), for automatic application of Hodapp-Parrish-Anderson, is presented and validated in a cross-sectional study. GFDC exhibits perfect accuracy in classifying mild, moderate, and severe glaucomatous field defects. GFDC may thereby improve the accuracy and fairness of clinical decision-making in glaucoma. The application and its source code are freely hosted online for clinicians and researchers to use with glaucoma patients.

Integrated single-cell and bulk RNA-Seq analysis enhances prognostic accuracy of PD-1/PD-L1 immunotherapy response in lung adenocarcinoma through necroptotic anoikis gene signatures

In addition to presenting significant diagnostic and treatment challenges, lung adenocarcinoma (LUAD) is the most common form of lung cancer. Using scRNA-Seq and bulk RNA-Seq data, we identify three genes referred to as HMR, FAM83A, and KRT6A these genes are related to necroptotic anoikis-related gene expression. Initial validation, conducted on the GSE50081 dataset, demonstrated the model's ability to categorize LUAD patients into high-risk and low-risk groups with significant survival differences. This model was further applied to predict responses to PD-1/PD-L1 blockade therapies, utilizing the IMvigor210 and GSE78220 cohorts, and showed strong correlation with patient outcomes, highlighting its potential in personalized immunotherapy. Further, LUAD cell lines were analyzed using quantitative PCR (qPCR) and Western blot analysis to confirm their expression levels, further corroborating the model's relevance in LUAD pathophysiology. The mutation landscape of these genes was also explored, revealing their broad implication in various cancer types through a pan-cancer analysis. The study also delved into molecular subclustering, revealing distinct expression profiles and associations with different survival outcomes, emphasizing the model’s utility in precision oncology. Moreover, the diversity of immune cell infiltration, analyzed in relation to the necroptotic anoikis signature, suggested significant implications for immune evasion mechanisms in LUAD. While the findings present a promising stride towards personalized LUAD treatment, especially in immunotherapy, limitations such as the retrospective nature of the datasets and the need for larger sample sizes are acknowledged. Prospective clinical trials and further experimental research are essential to validate these findings and enhance the clinical applicability of our prognostic model.

Prognostic Value and Determinants of High-Sensitivity Cardiac Troponin T in Patients With a Systemic Right Ventricle: Insights From the SERVE Trial.

The determinants and prognostic value of high-sensitivity cardiac troponin T (hs-cTnT) among patients with a systemic right ventricle are largely unknown. Ninety-eight patients from the randomized controlled SERVE (Effect of Phosphodiesterase-5 Inhibition With Tadalafil on Systemic Right Ventricular Size and Function) trial were included. The correlation between baseline hs-cTnT concentrations and biventricular volumes and function quantified by cardiac magnetic resonance or cardiac multirow detector computed tomography was assessed by adjusted linear regression models. The prognostic value of hs-cTnT was assessed by adjusted Cox proportional hazards models, survival analysis, and concordance statistics. The primary outcome was time to the composite of clinically relevant arrhythmia, hospitalization for heart failure, or all-cause death. Median age was 39 (interquartile range, 32-48) years, and 32% were women. Median hs-cTnT concentration was 7 (interquartile range, 4-11) ng/L. Coefficients of determination for the relationship between hs-cTnT concentrations and right ventricular end-systolic volume index and right ventricular ejection fraction (RVEF) were +0.368 (P=0.046) and -0.381 (P=0.018), respectively. The sex- and age-adjusted hazard ratio for the primary outcome of hs-cTnT at 2 and 4 times the reference level (5 ng/L) were 2.89 (95% CI, 1.14-7.29) and 4.42 (95% CI, 1.21-16.15), respectively. The prognostic performance quantified by the concordance statistics for age- and sex-adjusted models based on hs-cTnT, right ventricular ejection fraction, and peak oxygen uptake predicted were comparable: 0.71% (95% CI, 0.61-0.82), 0.72% (95% CI, 0.59-0.84), and 0.71% (95% CI, 0.59-0.83), respectively. Hs-cTnT concentration was significantly correlated with right ventricular ejection fraction and right ventricular end-systolic volume index in patients with a systemic right ventricle. The prognostic accuracy of hs-cTnT was comparable to that of right ventricular ejection fraction and peak oxygen uptake predicted. URL: https://www.clinicaltrials.gov; Unique identifier: NCT03049540.

Could internal jugular vein ultrasound be useful in the assessment of patients with heart failure? A systematic review

Many recent studies have validated the internal jugular vein ultrasound (IJV-US) ultrasound for estimating volemic status in critical patients. If research confirms its accuracy in detecting intravascular volume, congestion, and prognosis, this method could help manage heart failure (HF) because it is simple, fast, and applicable in several settings. This review examines the IJV-US’s reliability, diagnostic accuracy, and ability to predict poor outcomes for HF, as well as its correlation with surrogate congestion tests like natriuretic peptides and elevated central venous pressure (CVP). Our systematic review followed the PRISMA statement for systematic reviews and meta-analyses. Eleven studies examined the reliability and validity of IJV-US measures in predicting congestion, volume status, and prognosis in adult spontaneously breathing HF patients. We searched PubMed, Cochrane Library, and EMBASE. IJV-US measurements using Valsalva and sniffing seem more accurate for predicting congestion, volume status, high CVP, and poor prognosis. The IJV ratio in chronic HF and out-hospital patients and the absence of IJV area [cross-sectional area (CSA)] changes in acute HF (AHF) and in-hospital patients are the most validated measurements. No evidence suggests the method is reliable between and within raters. AHF patients’ IJV ratio and CSA absence appear to correlate with CVP and atrial natriuretic peptides. In conclusion, the IJV-US may be useful for managing HF patients, but more research is needed to confirm its reliability and prognostic accuracy. Develop a standard US protocol lastly.

Gender-based differences in clinical profile, management and mortality in cardiogenic shock. Prognostic accuracy of CardShock vs IABP Score, the Shock-CAT study

Abstract Funding Acknowledgements None. Background Mortality in cardiogenic shock (CS) remains very high. Gender could influence in aetiology, management, outcomes and risk stratification predictors. Purpose The aim is to analyse differences in clinical profile, management, in-hospital prognosis and mortality risk in CS patients depending on sex and compare the prognostic accuracy of CardShock and IABP scores between sexes in a Mediterranean cohort. Method Shock-CAT study was a multicentre, prospective, observational study conducted between December 2018 and December 2019 in eight public University hospitals in Spain, including CS patients of different aetiologies. Data on clinical presentation, biomarkers, management, including mechanical cardiac/circulatory support (MCS) were analysed comparing women and men. Cardshock and IABP score have been compared to assess 90-days mortality risk between sexes. Results A total of 382 CS patients (p) were included: women 24.9% (n=95 p) and men 75.1% (n=287p). Women were older than men (68.9 vs 64.1 years, P=0.004). CS due to acute coronary syndrome (ACS) was more frequent in men (66.6% vs 43.2%), and myocarditis (1.7 vs 6.3%), stress cardiomyopathy (0.3% vs 8.4%) and severe aortic stenosis (2.4% vs 6.3%) were more prevalent in women, all p&amp;lt;0.001. Cardiac arrest was more prevalent in men than women (38.4% vs 25.3%, p=0.020). MCS was implanted in 35.3% of patients, without gender differences (men 36.6%, women 31.6%, p=0.376). Even though there was a non-significant trend to lower use in women of IABP (31.1% vs 26.5%), Impella (13.5% vs 7.4%) or ECMO (10.4% vs 7.4%). Risk scores were similar between sexes: Cardshock (men:4.32 vs 4.40 women, p=0.674) and IABP score (men: 2.31 vs 2.04 women, p=0.190). There were no differences between men and women in in-hospital mortality (32.4% vs 34.7%, p=0.675) or 6-months mortality (41.5% vs 40%, p=0.989). Receiver-operating characteristic curves demonstrated that IABP shock score had superior prognostic power for predicting 90-days mortality when compared with Cardshock score in men (area under the curve -AUC- 0.72 vs 0.64) respectively, p=0.009, although both scores were similar in women (AUC 0.70 vs 0.72, p=0.635), Figures 1-2. Conclusions Women with CS were older than men and had less ACS and cardiac arrest at admission. There is a non-significant lower use of mechanical assistance devices in women, although in-hospital or 6-months mortality were similar in both sexes. IABP score provided better 90-days mortality risk prediction than CardShock score in men, but both scores were similar in women with cardiogenic shock.Men ROC Curves 90-days MortalityWomen ROC Curves 90-days Mortality

Machine Learning to Predict Outcomes of Fetal Cardiac Disease: A Pilot Study.

Prediction of outcomes following a prenatal diagnosis of congenital heart disease (CHD) is challenging. Machine learning (ML) algorithms may be used to reduce clinical uncertainty and improve prognostic accuracy. We performed a pilot study to train ML algorithms to predict postnatal outcomes based on clinical data. Specific objectives were to predict (1) in utero or neonatal death, (2) high-acuity neonatal care and (3) favorable outcomes. We included all fetuses with cardiac disease at Sunnybrook Health Sciences Centre, Toronto, Canada, from 2012 to 2021. Prediction models were created using the XgBoost algorithm (tree-based) with fivefold cross-validation. Among 211 cases of fetal cardiac disease, 61 were excluded (39 terminations, 21 lost to follow-up, 1 isolated arrhythmia), leaving a cohort of 150 fetuses. Fifteen (10%) demised (10 neonates) and 65 (48%) of live births required high acuity neonatal care. Of those with clinical follow-up, 60/87 (69%) had a favorable outcome. Prediction models for fetal or neonatal death, high acuity neonatal care and favorable outcome had AUCs of 0.76, 0.84 and 0.73, respectively. The most important predictors for death were the presence of non-cardiac abnormalities combined with more severe CHD. High acuity of postnatal care was predicted by anti Ro antibody and more severe CHD. Favorable outcome was most predicted by no right heart disease combined with genetic abnormalities, and maternal medications. Prediction models using ML provide good discrimination of key prenatal and postnatal outcomes among fetuses with congenital heart disease.

Nomogram for predicting survival in T1–T2 stage patients with supraglottic squamous cell carcinoma

BackgroundSupraglottic squamous cell carcinoma (SGSCC) is characterized by low differentiation, rapid growth, and inconspicuous initial manifestations. Early detection and prompt treatment can significantly improve survival rates. The main focus of treatment is to maintain optimal laryngeal function.MethodsUsing the Surveillance, Epidemiology, and End Results (SEER) database, we conducted univariate and multivariate Cox regression analyses to identify independent prognostic factors for T1–T2 SGSCC. We also enrolled 109 patients with T1–T2 SGSCC from the First Affiliated Hospital of Xinjiang Medical University as an external validation set. In addition, we developed a nomogram to predict the prognosis of T1–T2 SGSCC, assessed the predictive accuracy and discriminatory ability of the nomogram using the area under the curve (AUC), C-index, receiver operating characteristic (ROC) curve and calibration curve, and confirmed the clinical validity of the nomogram using decision curve analysis (DCA).ResultsOur investigation identified nine prognostic indicators for T1–T2 SGSCC: age (≥ 65 years), marital status, American Joint Committee on Cancer (AJCC) stage (II–IV), grade (III–IV), M stage (M1), radiotherapy, chemotherapy, sex (female), and surgery. These variables were used to create accurate nomograms that predict overall and specific survival rates at 1, 3, and 5 years. The nomograms demonstrated superior prognostic value and accuracy compared to AJCC staging. Laryngectomy with partial laryngectomy is the preferred treatment option for T1–T2 SGSCC cases, providing superior overall survival (OS) and cancer-specific survival (CSS). Radiotherapy also improves OS and CSS. Our results were based on a comprehensive analysis of various indicators, including the C-index, ROC curve, calibration curve, and DCA curve.ConclusionNomograms provide significant advantages in treatment decision making and diagnosis. Laryngectomy with partial laryngectomy is the most appropriate method for T1–T2 SGSCC cases. However, radiotherapy can also be used. Thus, patients with T1–T2 SGSCC should be evaluated to determine if combination therapy is the optimal treatment approach. Nevertheless, further research is needed to understand the role of chemotherapy. Overall, this study identified nine key predictors of future outcomes, aiding healthcare professionals in assessing risks and making treatment decisions for T1–T2 SGSCC patients.

Abstract PO5-27-01: Comparison between the 7th and the 8th edition of TNM Staging System of American joint committee on Cancer(AJCC) for Breast Cancer Patients, diagnosed and treated at King Abdulaziz Medical City

Abstract Breast cancer is a heterogeneous disease with different biological and molecular characteristics that affect local recurrence, metastases, prognoses, and response to available therapies. The 8th edition of the American Joint Committee on Cancer (AJCC) staging system for breast cancer has incorporated the biological and molecular characteristics, tumor grade, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor status(Her-2)and tumor grade, into the traditional anatomical 7th edition that only considers tumor size, lymph nodes status, and distant metastases. The aim of this study is to compare the 7th to 8th editions of the AJCC staging system for prognostic impact, assess the effect of the new edition on the treatment and treatment outcome Method A retrospective cohort study was performed for all breast cancer patients, diagnosed between (2013 -2019) and underwent upfront surgery at King Abdulaziz Medical City (KAMC).Patients’ clinicopathological, immunohistochemical, surgical, therapeutic, and follow-up data were retrieved. Overall survival (OS) and breast cancer-specific survival (BCSS) were estimated using the Kaplan–Meier method. Univariate analysis using Fisher's exact test was used to compare staging systems' prognostic accuracy. Multivariable analysis using Cox proportional hazards regression analysis was performed to identify factors independently associated with disease outcome Results A total of 169 female patients were included, the 8th AJCC staging system resulted in a reallocation of 108 (63.9%) of the patients to a different stage group. The majority were downstaged(60.9%) while only 3% were upstaged, most of the upstaged patients had a triple-negative subtype (80%). The change in overall stage for the 8th edition was most significant in stage I (an increase by 140.3%), followed by stage II (downstaged by 74.3%) and stage III (downstaged by 49.7%) compared to the 7th edition. Critically 42.8% of all downstaged patients treated with adjuvant chemotherapy were found to have received unnecessary chemotherapy after applying the 8th AJCC staging system as per National Comprehensive Cancer Network (NCCN) clinical practice guidelines recommendations. The 8th AJCC staging system was a better prognostic predictor of disease survival, patients in 8th AJCC stage 3 had a higher tendency to develop metastases and die because of their disease (P-value 0.03, 0.051, respectively). Conclusions: Our comparison revealed that the AJCC 8th edition staging system is superior and more comprehensive than the 7th, in term of prognostic value, and in defining the precise adjuvant therapy Figure 1 Comparison of the 7th AJCC and the 8th AJCC staging system Citation Format: Nafisa Abdelhafiez, Abdulmohsen Alkushi, Lolwah Alriyees, Mohammad Arabi, Faris Alsalamah, Emad Masuadi, Ahmad Omair, Mohammad Alkaiyat, Hussam Shehata. Comparison between the 7th and the 8th edition of TNM Staging System of American joint committee on Cancer(AJCC) for Breast Cancer Patients, diagnosed and treated at King Abdulaziz Medical City [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-27-01.

Abstract PO5-24-10: Clinical significance and immune landscape analyses of the immune related genes based prognostic signature for HER2 positive breast cancer

Abstract Objective: To date, significant breakthrough has yet to be achieved in immunotherapy for HER2-positive breast cancer (HPBC). In this study, we aimed to construct a risk model for predicting prognosis and therapy response and evaluating the tumor immune landscape in HPBC patients. Methods: Differentially expressed immune-related genes (IRGs) were used as input to weighted correlation network analysis (WGCNA) to identify modules of highly correlated gene. An optimal prognostic TPBC-IRGs signature were constructed by least absolute shrinkage and selection operator (LASSO) cox regression. Survival analysis and ROC curves were analyzed to identify the predictive value in a training cohort and external validation cohorts. With three GEO datasets and samples from our center, the anti-HER2 therapeutic efficacy in HPBC patients was predicted through this prognostic model. Additionally, the immunotherapy outcome was forecasted using IMvigor210 dataset. We also investigated the connection between high and low-risk populations and immune checkpoints and analyzed immune infiltration levels of these populations with several algorithms. Ultimately, we develop a combined model and nomogram by integrating the risk score with clinical factors. Results: 11 key genes associated with overall survival (OS) in the blue module were selected for further analysis. A 3-gene signature (CX3CL1, RARB, TANK) based prognostic risk model was developed. The Kaplan-Meier survival curve revealed that patients with high-risk score had shorter OS compared with the low-risk group. The AUC of the training cohort at 1-, 3- and 5-year were 0.718, 0.784 and 0.767. The performance of the risk model was validated with GSE25066 and GSE55348. It’s showed that the risk scores of HPBC who were resistant to trastuzumab were significantly higher than those of patients who were sensitive to trastuzumab. The results indicated that the levels of immune checkpoints expression (such as PD-1, CTLA4 and TIGIT) and immune infiltration were higher in low-risk group than in high-risk group. The combined model showed better prognostic prediction accuracy compared to the clinical model or gene signature alone (1-year AUC=0.910;3-year AUC=0.832;5-year AUC=0.882). Conclusion: The immune gene-based risk profile and combined prognostic model in this study may hold promise for clinical use in predicting the prognosis and treatment efficacy of HPBC patients. figure3. Construction of HPBC-IRGs signature based prognostic model. Citation Format: Xiaofen Li, Wenfen Fu, Yushuai Yu, Jie Zhang, Chuangui Song. Clinical significance and immune landscape analyses of the immune related genes based prognostic signature for HER2 positive breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-24-10.

Abstract PO4-02-12: MDA iLobular Prognostic Tool: A Novel Approach for Risk Stratification in Invasive Lobular Carcinoma

Abstract Background: Invasive lobular carcinoma (ILC) is a distinct subtype of breast cancer that differs from the more common invasive ductal carcinoma (IDC) by its unique characteristics that influence prognosis. Recent studies have shown that ILC is associated with worse long-term outcomes compared to IDC. Risk stratification is essential in breast cancer for tailoring treatment plans to the individual patient’s needs, maximizing the chances of successful outcomes and minimizing unnecessary interventions. Although there are multiple tools available for breast cancer, there is currently not a risk stratification tool that is specific for ILC. The MDA iLobular prognostic tool is the first risk stratification tool that has been developed specifically for ILC patients. Methods: We retrospectively searched for patients treated at MD Anderson Cancer Center with a diagnosis of stage I-III ILC in our prospectively collected electronic database. The study focused on two primary endpoints: Overall Survival (OS) and Distant Recurrence Free Survival (DRFS). We utilized univariate and multivariate Cox Proportional Hazard (PH) regression models to assess the statistical significance of all variables. The univariate Cox analysis identified prognostic factors, which were further analyzed using backward and stepwise multivariate Cox proportional hazards regression analysis. This process helped identify statistically significant prognostic factors that were included in the final multivariate models. We estimated hazard ratios and 95% confidence intervals for each factor, considering a P-value of &amp;lt; 0.05 as statistically significant. To evaluate the performance of the fitted multivariate Cox PH regression models for OS and DRFS, we randomly divided two-thirds of the data points into a training dataset, while the remaining one-third constituted the test dataset. We assessed the discrimination capacity of each model using Harrell's C-index. Results: The study included a total of 4,216 female patients, with a median age of 56 years. The median pathological tumor size was 2 mm, and the median number of lymph nodes was one. Among these patients, 1,376 were pre-menopausal, while 2,837 were post-menopausal. The training cohort was a subset of 2,950 patients and the test cohort was a subset of 1,266 patients. After evaluating various prognostic models, we identified the model with the highest prognostic accuracy for OS and DRFS. This selected model demonstrated a Harrell's C-index of 0.704 for OS and 0.718 for DRFS on the training dataset and a Harrell's C-index of 0.702 for OS and 0.671 for DRFS on the test dataset. The model incorporated several covariates, including age at the time of diagnosis, number of lymph nodes, pathological tumor size (mm), ER status, tumor grade, ILC histology, and the presence or absence of concomitant LCIS (Table 1). Conclusion: In conclusion, the MDA iLobular prognostic tool represents a significant advancement as the first dedicated tool for risk stratifying ILC, providing valuable guidance for tailoring therapy to individual ILC patients based on their specific risk profiles. Table 1. Multivariate Cox proportional hazard model parameter estimates for OS and DRFS Citation Format: Jason Mouabbi, Sarah Pasyar, Roland Bassett, Akshara Singareeka Raghavendra, Amy Hassan, Rachel Layman, Debu Tripathy. MDA iLobular Prognostic Tool: A Novel Approach for Risk Stratification in Invasive Lobular Carcinoma [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-02-12.